Key points are not available for this paper at this time.
With the end of Moore’s law in sight, new computing architectures are urgently needed to satisfy the increasing demands for big data processing. Neuromorphic architectures with photoelectric learning capability are good candidates for energy-efficient computing for recognition and classification tasks. In this work, artificial synapses based on the ZnO1–x/AlOy heterojunction were fabricated and the photoelectric plasticity was investigated. Versatile synaptic functions such as photoelectric short-term/long-term plasticity, paired-pulse facilitation, neuromorphic facilitation, and depression were emulated based on the inherent persistent photoconductivity and volatile resistive switching characteristics of the device. It is found that the naturally formed AlOy layer provides traps for photogenerated holes, resulting in a significant persistent photoconductivity effect. Moreover, the resistive switching can be attributed to the electron trapping/detrapping at the trapping sites in the AlOy layer.
Hu et al. (Thu,) studied this question.